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      "source": [
        "# Residuals Networks and Transfer Learning\n",
        "\n",
        "\n",
        "In this notebook we are going to implement a simple convolutional neural network as well as CNN with residual connections. Additionally we will also take a look at Transfer Learning.\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SharifiZarchi/Introduction_to_Machine_Learning/blob/main/Jupyter_Notebooks/Chapter_04_Computer_Vision/ResNets_Transfer_Learning.ipynb)\n",
        "[![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/SharifiZarchi/Introduction_to_Machine_Learning/main/Jupyter_Notebooks/Chapter_04_Computer_Vision/ResNets_Transfer_Learning.ipynb)"
      ],
      "metadata": {
        "id": "axViOHHuSBe5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title setup and imports\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from tqdm import trange\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.optim import Adam\n",
        "from torch.nn import CrossEntropyLoss\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "from torchvision import transforms, models\n",
        "from torchvision.datasets.cifar import CIFAR10\n",
        "\n",
        "from torchsummary import summary\n",
        "\n",
        "torch.manual_seed(0)\n",
        "\n",
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')"
      ],
      "metadata": {
        "id": "CYQYyHliSpeZ",
        "execution": {
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    {
      "cell_type": "code",
      "source": [
        "# @title helper functions\n",
        "\n",
        "def train_epoch(model, dataloader, loss_fn, optimizer, device):\n",
        "    model.train()\n",
        "    train_loss = 0.\n",
        "    train_acc = 0.\n",
        "    for images, labels in dataloader:\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        logits = model(images)\n",
        "        loss = loss_fn(logits, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        train_loss += loss.item()\n",
        "        train_acc += (logits.argmax(dim=1) == labels).sum().item()\n",
        "    train_loss /= len(dataloader)\n",
        "    train_acc /= len(dataloader.dataset)\n",
        "    return train_loss, train_acc\n",
        "\n",
        "\n",
        "def validate_epoch(model, dataloader, loss_fn, device):\n",
        "    model.eval()\n",
        "    val_loss = 0.\n",
        "    val_acc = 0.\n",
        "    with torch.inference_mode():\n",
        "        for images, labels in dataloader:\n",
        "            images, labels = images.to(device), labels.to(device)\n",
        "            logits = model(images)\n",
        "            loss = loss_fn(logits, labels)\n",
        "            val_loss += loss.item()\n",
        "            val_acc += (logits.argmax(dim=1) == labels).sum().item()\n",
        "        val_loss /= len(dataloader)\n",
        "        val_acc /= len(dataloader.dataset)\n",
        "    return val_loss, val_acc\n",
        "\n",
        "\n",
        "def train_model(model, train_dataloader, val_dataloader, optimizer, n_epochs, device=device):\n",
        "    history = {'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': []}\n",
        "    loss_fn = CrossEntropyLoss()\n",
        "    for _ in (pbar := trange(n_epochs)):\n",
        "        train_loss, train_acc = train_epoch(model, train_dataloader, loss_fn, optimizer, device)\n",
        "        history['train_loss'].append(train_loss), history['train_acc'].append(train_acc)\n",
        "        val_loss, val_acc = validate_epoch(model, val_dataloader, loss_fn, device)\n",
        "        history['val_loss'].append(val_loss), history['val_acc'].append(val_acc)\n",
        "        pbar.set_description(f'Training Accuracy {100 * train_acc:.2f}% | Validation Accuracy {100 * val_acc:.2f}% ')\n",
        "    return history\n",
        "\n",
        "\n",
        "def plot_history(history):\n",
        "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
        "    ax1.plot(history['train_loss'], label='train')\n",
        "    ax1.plot(history['val_loss'], label='val')\n",
        "    ax1.set_xlabel('Epochs')\n",
        "    ax1.set_ylabel('Loss')\n",
        "    ax1.legend()\n",
        "\n",
        "    ax2.plot(history['train_acc'], label='train')\n",
        "    ax2.plot(history['val_acc'], label='val')\n",
        "    ax2.set_xlabel('Epochs')\n",
        "    ax2.set_ylabel('Accuracy')\n",
        "    ax2.legend()\n",
        "    plt.show()"
      ],
      "metadata": {
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        "execution": {
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    {
      "cell_type": "code",
      "source": [
        "# @title CIFAR10 dataset\n",
        "\n",
        "norm_mean = (0.4914, 0.4822, 0.4465)\n",
        "norm_std = (0.2023, 0.1994, 0.2010)\n",
        "batch_size = 128\n",
        "\n",
        "transform_train = transforms.Compose([\n",
        "    transforms.RandomCrop(32, padding=4),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(norm_mean, norm_std),\n",
        "])\n",
        "\n",
        "transform_test = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(norm_mean, norm_std),\n",
        "])\n",
        "\n",
        "trainset = CIFAR10(root='./data', train=True, download=True, transform=transform_train)\n",
        "trainloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
        "\n",
        "testset = CIFAR10(root='./data', train=False, download=True, transform=transform_test)\n",
        "testloader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=2)\n",
        "\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat', 'deer',\n",
        "           'dog', 'frog', 'horse', 'ship', 'truck')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "4-ORDaFPSpXz",
        "outputId": "5f0589f1-8b89-4ec1-ee5b-e78ac257ef40",
        "execution": {
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        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Files already downloaded and verified\nFiles already downloaded and verified\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Convolutional Neural Networks"
      ],
      "metadata": {
        "id": "sEFVbOtbSofo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's start by creating `BasicBlock`s for the CNN. We will create our blocks as follows:\n",
        "\n",
        "\n",
        "*   Each block will have 3 convolutional layers with `kernel_size=3` and `padding=1`\n",
        "*   Each convolutional layer will be followed by a batch normalization layer and a `ReLU` activation function\n",
        "*   At the end of each block, we will apply a max pooling layer to reduce the dimensions by a factor of 2\n",
        "\n"
      ],
      "metadata": {
        "id": "oUv2AhkeT8uc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BasicBlock(nn.Module):\n",
        "    def __init__(self, in_channels: int, out_channels: int) -> None:\n",
        "\n",
        "        super(BasicBlock, self).__init__()\n",
        "\n",
        "        self.block = nn.Sequential(\n",
        "            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "        )\n",
        "\n",
        "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
        "        out = self.block(x)\n",
        "        return  out"
      ],
      "metadata": {
        "id": "U5FNgUJyztkp",
        "execution": {
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      "execution_count": null,
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Next we will proceed to the network itself. Using the `BasicBlock` module, we first create three blocks and double the number of channels at each one (except for the first block which has an output channel size of 64). At last we use an average pooling layer in addition to a fully connected layer to make the predictions."
      ],
      "metadata": {
        "id": "esNaCFsG26Wn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Network(nn.Module):\n",
        "    def __init__(self, img_channels: int = 3, num_classes: int = 10, block: nn.Module = BasicBlock) -> None:\n",
        "\n",
        "        super(Network, self).__init__()\n",
        "\n",
        "        self.block1 = block(img_channels, 64)\n",
        "        self.block2 = block(64, 128)\n",
        "        self.block3 = block(128, 256)\n",
        "        self.block4 = block(256, 512)\n",
        "\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
        "\n",
        "        self.fc = nn.Linear(512, num_classes)\n",
        "\n",
        "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
        "        x = self.block1(x)\n",
        "        x = self.block2(x)\n",
        "        x = self.block3(x)\n",
        "        x = self.block4(x)\n",
        "        x = self.avgpool(x)\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = self.fc(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "Nhs46GiESDnk",
        "execution": {
          "iopub.status.busy": "2024-09-16T22:31:46.516103Z",
          "iopub.execute_input": "2024-09-16T22:31:46.516379Z",
          "iopub.status.idle": "2024-09-16T22:31:46.529842Z",
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        },
        "trusted": true
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets create an instance of this architecture and see its parameter count."
      ],
      "metadata": {
        "id": "P6uuFzB1fD0K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cnn = Network().to(device)\n",
        "summary(cnn, (3, 32, 32))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sq0OGQA2SBGW",
        "outputId": "809ad3f0-f401-425e-ec77-98360f6783b5",
        "execution": {
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      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "==========================================================================================\nLayer (type:depth-idx)                   Output Shape              Param #\n==========================================================================================\n├─BasicBlock: 1-1                        [-1, 64, 16, 16]          --\n|    └─Sequential: 2-1                   [-1, 64, 16, 16]          --\n|    |    └─Conv2d: 3-1                  [-1, 64, 32, 32]          1,792\n|    |    └─BatchNorm2d: 3-2             [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-3                    [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-4                  [-1, 64, 32, 32]          36,928\n|    |    └─BatchNorm2d: 3-5             [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-6                    [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-7                  [-1, 64, 32, 32]          36,928\n|    |    └─BatchNorm2d: 3-8             [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-9                    [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-10                 [-1, 64, 32, 32]          36,928\n|    |    └─BatchNorm2d: 3-11            [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-12                   [-1, 64, 32, 32]          --\n|    |    └─MaxPool2d: 3-13              [-1, 64, 16, 16]          --\n├─BasicBlock: 1-2                        [-1, 128, 8, 8]           --\n|    └─Sequential: 2-2                   [-1, 128, 8, 8]           --\n|    |    └─Conv2d: 3-14                 [-1, 128, 16, 16]         73,856\n|    |    └─BatchNorm2d: 3-15            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-16                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-17                 [-1, 128, 16, 16]         147,584\n|    |    └─BatchNorm2d: 3-18            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-19                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-20                 [-1, 128, 16, 16]         147,584\n|    |    └─BatchNorm2d: 3-21            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-22                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-23                 [-1, 128, 16, 16]         147,584\n|    |    └─BatchNorm2d: 3-24            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-25                   [-1, 128, 16, 16]         --\n|    |    └─MaxPool2d: 3-26              [-1, 128, 8, 8]           --\n├─BasicBlock: 1-3                        [-1, 256, 4, 4]           --\n|    └─Sequential: 2-3                   [-1, 256, 4, 4]           --\n|    |    └─Conv2d: 3-27                 [-1, 256, 8, 8]           295,168\n|    |    └─BatchNorm2d: 3-28            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-29                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-30                 [-1, 256, 8, 8]           590,080\n|    |    └─BatchNorm2d: 3-31            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-32                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-33                 [-1, 256, 8, 8]           590,080\n|    |    └─BatchNorm2d: 3-34            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-35                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-36                 [-1, 256, 8, 8]           590,080\n|    |    └─BatchNorm2d: 3-37            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-38                   [-1, 256, 8, 8]           --\n|    |    └─MaxPool2d: 3-39              [-1, 256, 4, 4]           --\n├─BasicBlock: 1-4                        [-1, 512, 2, 2]           --\n|    └─Sequential: 2-4                   [-1, 512, 2, 2]           --\n|    |    └─Conv2d: 3-40                 [-1, 512, 4, 4]           1,180,160\n|    |    └─BatchNorm2d: 3-41            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-42                   [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-43                 [-1, 512, 4, 4]           2,359,808\n|    |    └─BatchNorm2d: 3-44            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-45                   [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-46                 [-1, 512, 4, 4]           2,359,808\n|    |    └─BatchNorm2d: 3-47            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-48                   [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-49                 [-1, 512, 4, 4]           2,359,808\n|    |    └─BatchNorm2d: 3-50            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-51                   [-1, 512, 4, 4]           --\n|    |    └─MaxPool2d: 3-52              [-1, 512, 2, 2]           --\n├─AdaptiveAvgPool2d: 1-5                 [-1, 512, 1, 1]           --\n├─Linear: 1-6                            [-1, 10]                  5,130\n==========================================================================================\nTotal params: 10,966,986\nTrainable params: 10,966,986\nNon-trainable params: 0\nTotal mult-adds (M): 533.29\n==========================================================================================\nInput size (MB): 0.01\nForward/backward pass size (MB): 7.50\nParams size (MB): 41.84\nEstimated Total Size (MB): 49.35\n==========================================================================================\n",
          "output_type": "stream"
        },
        {
          "execution_count": 6,
          "output_type": "execute_result",
          "data": {
            "text/plain": "==========================================================================================\nLayer (type:depth-idx)                   Output Shape              Param #\n==========================================================================================\n├─BasicBlock: 1-1                        [-1, 64, 16, 16]          --\n|    └─Sequential: 2-1                   [-1, 64, 16, 16]          --\n|    |    └─Conv2d: 3-1                  [-1, 64, 32, 32]          1,792\n|    |    └─BatchNorm2d: 3-2             [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-3                    [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-4                  [-1, 64, 32, 32]          36,928\n|    |    └─BatchNorm2d: 3-5             [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-6                    [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-7                  [-1, 64, 32, 32]          36,928\n|    |    └─BatchNorm2d: 3-8             [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-9                    [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-10                 [-1, 64, 32, 32]          36,928\n|    |    └─BatchNorm2d: 3-11            [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-12                   [-1, 64, 32, 32]          --\n|    |    └─MaxPool2d: 3-13              [-1, 64, 16, 16]          --\n├─BasicBlock: 1-2                        [-1, 128, 8, 8]           --\n|    └─Sequential: 2-2                   [-1, 128, 8, 8]           --\n|    |    └─Conv2d: 3-14                 [-1, 128, 16, 16]         73,856\n|    |    └─BatchNorm2d: 3-15            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-16                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-17                 [-1, 128, 16, 16]         147,584\n|    |    └─BatchNorm2d: 3-18            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-19                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-20                 [-1, 128, 16, 16]         147,584\n|    |    └─BatchNorm2d: 3-21            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-22                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-23                 [-1, 128, 16, 16]         147,584\n|    |    └─BatchNorm2d: 3-24            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-25                   [-1, 128, 16, 16]         --\n|    |    └─MaxPool2d: 3-26              [-1, 128, 8, 8]           --\n├─BasicBlock: 1-3                        [-1, 256, 4, 4]           --\n|    └─Sequential: 2-3                   [-1, 256, 4, 4]           --\n|    |    └─Conv2d: 3-27                 [-1, 256, 8, 8]           295,168\n|    |    └─BatchNorm2d: 3-28            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-29                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-30                 [-1, 256, 8, 8]           590,080\n|    |    └─BatchNorm2d: 3-31            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-32                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-33                 [-1, 256, 8, 8]           590,080\n|    |    └─BatchNorm2d: 3-34            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-35                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-36                 [-1, 256, 8, 8]           590,080\n|    |    └─BatchNorm2d: 3-37            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-38                   [-1, 256, 8, 8]           --\n|    |    └─MaxPool2d: 3-39              [-1, 256, 4, 4]           --\n├─BasicBlock: 1-4                        [-1, 512, 2, 2]           --\n|    └─Sequential: 2-4                   [-1, 512, 2, 2]           --\n|    |    └─Conv2d: 3-40                 [-1, 512, 4, 4]           1,180,160\n|    |    └─BatchNorm2d: 3-41            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-42                   [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-43                 [-1, 512, 4, 4]           2,359,808\n|    |    └─BatchNorm2d: 3-44            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-45                   [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-46                 [-1, 512, 4, 4]           2,359,808\n|    |    └─BatchNorm2d: 3-47            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-48                   [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-49                 [-1, 512, 4, 4]           2,359,808\n|    |    └─BatchNorm2d: 3-50            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-51                   [-1, 512, 4, 4]           --\n|    |    └─MaxPool2d: 3-52              [-1, 512, 2, 2]           --\n├─AdaptiveAvgPool2d: 1-5                 [-1, 512, 1, 1]           --\n├─Linear: 1-6                            [-1, 10]                  5,130\n==========================================================================================\nTotal params: 10,966,986\nTrainable params: 10,966,986\nNon-trainable params: 0\nTotal mult-adds (M): 533.29\n==========================================================================================\nInput size (MB): 0.01\nForward/backward pass size (MB): 7.50\nParams size (MB): 41.84\nEstimated Total Size (MB): 49.35\n=========================================================================================="
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally we can train our model."
      ],
      "metadata": {
        "id": "RNnO05Mbfvj-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if device == 'cuda':\n",
        "    cnn = torch.compile(cnn)\n",
        "optim = Adam(cnn.parameters(), lr=1e-3)\n",
        "\n",
        "results = train_model(cnn, trainloader, testloader, optim, n_epochs=30)\n",
        "plot_history(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "Ly5revaMRkxd",
        "outputId": "c9c300d8-8833-429e-e023-33ef6dc5db04",
        "execution": {
          "iopub.status.busy": "2024-09-16T22:31:46.982939Z",
          "iopub.execute_input": "2024-09-16T22:31:46.983354Z",
          "iopub.status.idle": "2024-09-16T22:49:17.210992Z",
          "shell.execute_reply.started": "2024-09-16T22:31:46.983309Z",
          "shell.execute_reply": "2024-09-16T22:49:17.209886Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "text": "Training Accuracy 95.18% | Validation Accuracy 89.33% : 100%|██████████| 30/30 [17:29<00:00, 34.99s/it]\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x500 with 2 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Residual Networks"
      ],
      "metadata": {
        "id": "D28V07oJgBBM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this section we are going to implement CNNs with residual connections. To do so we will first implement a `ResidualBlock` module. In each block we must first apply a convolutional layer so that the `in_channels` and `out_channels` match. We will keep a copy of the output of this convolutional layer and add it to the output of the next two convolutions. After that we can apply a max pooling layer to reduce the dimensions of the input."
      ],
      "metadata": {
        "id": "evhe65ySsTee"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ResidualBlock(nn.Module):\n",
        "    def __init__(self, in_channels: int, out_channels: int) -> None:\n",
        "\n",
        "        super(ResidualBlock, self).__init__()\n",
        "\n",
        "        self.conv = nn.Sequential(\n",
        "            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "        )\n",
        "\n",
        "        self.block = nn.Sequential(\n",
        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "        )\n",
        "\n",
        "        self.downsample = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "\n",
        "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
        "        identity = self.conv(x)\n",
        "        out = self.block(identity) + identity\n",
        "        out = self.downsample(out)\n",
        "        return  out"
      ],
      "metadata": {
        "id": "W_dlvd8ZgCuS",
        "execution": {
          "iopub.status.busy": "2024-09-16T22:49:17.212538Z",
          "iopub.execute_input": "2024-09-16T22:49:17.212868Z",
          "iopub.status.idle": "2024-09-16T22:49:17.222596Z",
          "shell.execute_reply.started": "2024-09-16T22:49:17.212833Z",
          "shell.execute_reply": "2024-09-16T22:49:17.221631Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the `ResidualBlock` module we just defined and the `Network` module from the previous section, we can now build a residual network."
      ],
      "metadata": {
        "id": "LEYcOzfiuqmr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "resnet = Network(block=ResidualBlock).to(device)\n",
        "summary(resnet, (3, 32, 32))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNDTqdt_gCcs",
        "outputId": "5720f7a1-b844-41b2-c560-d63ba3a9ddaa",
        "execution": {
          "iopub.status.busy": "2024-09-16T22:49:17.223813Z",
          "iopub.execute_input": "2024-09-16T22:49:17.224146Z",
          "iopub.status.idle": "2024-09-16T22:49:17.370523Z",
          "shell.execute_reply.started": "2024-09-16T22:49:17.224114Z",
          "shell.execute_reply": "2024-09-16T22:49:17.369639Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "==========================================================================================\nLayer (type:depth-idx)                   Output Shape              Param #\n==========================================================================================\n├─ResidualBlock: 1-1                     [-1, 64, 16, 16]          --\n|    └─Sequential: 2-1                   [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-1                  [-1, 64, 32, 32]          1,792\n|    |    └─BatchNorm2d: 3-2             [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-3                    [-1, 64, 32, 32]          --\n|    └─Sequential: 2-2                   [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-4                  [-1, 64, 32, 32]          36,928\n|    |    └─BatchNorm2d: 3-5             [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-6                    [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-7                  [-1, 64, 32, 32]          36,928\n|    |    └─BatchNorm2d: 3-8             [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-9                    [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-10                 [-1, 64, 32, 32]          36,928\n|    |    └─BatchNorm2d: 3-11            [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-12                   [-1, 64, 32, 32]          --\n|    └─MaxPool2d: 2-3                    [-1, 64, 16, 16]          --\n├─ResidualBlock: 1-2                     [-1, 128, 8, 8]           --\n|    └─Sequential: 2-4                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-13                 [-1, 128, 16, 16]         73,856\n|    |    └─BatchNorm2d: 3-14            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-15                   [-1, 128, 16, 16]         --\n|    └─Sequential: 2-5                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-16                 [-1, 128, 16, 16]         147,584\n|    |    └─BatchNorm2d: 3-17            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-18                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-19                 [-1, 128, 16, 16]         147,584\n|    |    └─BatchNorm2d: 3-20            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-21                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-22                 [-1, 128, 16, 16]         147,584\n|    |    └─BatchNorm2d: 3-23            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-24                   [-1, 128, 16, 16]         --\n|    └─MaxPool2d: 2-6                    [-1, 128, 8, 8]           --\n├─ResidualBlock: 1-3                     [-1, 256, 4, 4]           --\n|    └─Sequential: 2-7                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-25                 [-1, 256, 8, 8]           295,168\n|    |    └─BatchNorm2d: 3-26            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-27                   [-1, 256, 8, 8]           --\n|    └─Sequential: 2-8                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-28                 [-1, 256, 8, 8]           590,080\n|    |    └─BatchNorm2d: 3-29            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-30                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-31                 [-1, 256, 8, 8]           590,080\n|    |    └─BatchNorm2d: 3-32            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-33                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-34                 [-1, 256, 8, 8]           590,080\n|    |    └─BatchNorm2d: 3-35            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-36                   [-1, 256, 8, 8]           --\n|    └─MaxPool2d: 2-9                    [-1, 256, 4, 4]           --\n├─ResidualBlock: 1-4                     [-1, 512, 2, 2]           --\n|    └─Sequential: 2-10                  [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-37                 [-1, 512, 4, 4]           1,180,160\n|    |    └─BatchNorm2d: 3-38            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-39                   [-1, 512, 4, 4]           --\n|    └─Sequential: 2-11                  [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-40                 [-1, 512, 4, 4]           2,359,808\n|    |    └─BatchNorm2d: 3-41            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-42                   [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-43                 [-1, 512, 4, 4]           2,359,808\n|    |    └─BatchNorm2d: 3-44            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-45                   [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-46                 [-1, 512, 4, 4]           2,359,808\n|    |    └─BatchNorm2d: 3-47            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-48                   [-1, 512, 4, 4]           --\n|    └─MaxPool2d: 2-12                   [-1, 512, 2, 2]           --\n├─AdaptiveAvgPool2d: 1-5                 [-1, 512, 1, 1]           --\n├─Linear: 1-6                            [-1, 10]                  5,130\n==========================================================================================\nTotal params: 10,966,986\nTrainable params: 10,966,986\nNon-trainable params: 0\nTotal mult-adds (M): 533.29\n==========================================================================================\nInput size (MB): 0.01\nForward/backward pass size (MB): 7.50\nParams size (MB): 41.84\nEstimated Total Size (MB): 49.35\n==========================================================================================\n",
          "output_type": "stream"
        },
        {
          "execution_count": 9,
          "output_type": "execute_result",
          "data": {
            "text/plain": "==========================================================================================\nLayer (type:depth-idx)                   Output Shape              Param #\n==========================================================================================\n├─ResidualBlock: 1-1                     [-1, 64, 16, 16]          --\n|    └─Sequential: 2-1                   [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-1                  [-1, 64, 32, 32]          1,792\n|    |    └─BatchNorm2d: 3-2             [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-3                    [-1, 64, 32, 32]          --\n|    └─Sequential: 2-2                   [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-4                  [-1, 64, 32, 32]          36,928\n|    |    └─BatchNorm2d: 3-5             [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-6                    [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-7                  [-1, 64, 32, 32]          36,928\n|    |    └─BatchNorm2d: 3-8             [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-9                    [-1, 64, 32, 32]          --\n|    |    └─Conv2d: 3-10                 [-1, 64, 32, 32]          36,928\n|    |    └─BatchNorm2d: 3-11            [-1, 64, 32, 32]          128\n|    |    └─ReLU: 3-12                   [-1, 64, 32, 32]          --\n|    └─MaxPool2d: 2-3                    [-1, 64, 16, 16]          --\n├─ResidualBlock: 1-2                     [-1, 128, 8, 8]           --\n|    └─Sequential: 2-4                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-13                 [-1, 128, 16, 16]         73,856\n|    |    └─BatchNorm2d: 3-14            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-15                   [-1, 128, 16, 16]         --\n|    └─Sequential: 2-5                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-16                 [-1, 128, 16, 16]         147,584\n|    |    └─BatchNorm2d: 3-17            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-18                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-19                 [-1, 128, 16, 16]         147,584\n|    |    └─BatchNorm2d: 3-20            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-21                   [-1, 128, 16, 16]         --\n|    |    └─Conv2d: 3-22                 [-1, 128, 16, 16]         147,584\n|    |    └─BatchNorm2d: 3-23            [-1, 128, 16, 16]         256\n|    |    └─ReLU: 3-24                   [-1, 128, 16, 16]         --\n|    └─MaxPool2d: 2-6                    [-1, 128, 8, 8]           --\n├─ResidualBlock: 1-3                     [-1, 256, 4, 4]           --\n|    └─Sequential: 2-7                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-25                 [-1, 256, 8, 8]           295,168\n|    |    └─BatchNorm2d: 3-26            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-27                   [-1, 256, 8, 8]           --\n|    └─Sequential: 2-8                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-28                 [-1, 256, 8, 8]           590,080\n|    |    └─BatchNorm2d: 3-29            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-30                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-31                 [-1, 256, 8, 8]           590,080\n|    |    └─BatchNorm2d: 3-32            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-33                   [-1, 256, 8, 8]           --\n|    |    └─Conv2d: 3-34                 [-1, 256, 8, 8]           590,080\n|    |    └─BatchNorm2d: 3-35            [-1, 256, 8, 8]           512\n|    |    └─ReLU: 3-36                   [-1, 256, 8, 8]           --\n|    └─MaxPool2d: 2-9                    [-1, 256, 4, 4]           --\n├─ResidualBlock: 1-4                     [-1, 512, 2, 2]           --\n|    └─Sequential: 2-10                  [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-37                 [-1, 512, 4, 4]           1,180,160\n|    |    └─BatchNorm2d: 3-38            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-39                   [-1, 512, 4, 4]           --\n|    └─Sequential: 2-11                  [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-40                 [-1, 512, 4, 4]           2,359,808\n|    |    └─BatchNorm2d: 3-41            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-42                   [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-43                 [-1, 512, 4, 4]           2,359,808\n|    |    └─BatchNorm2d: 3-44            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-45                   [-1, 512, 4, 4]           --\n|    |    └─Conv2d: 3-46                 [-1, 512, 4, 4]           2,359,808\n|    |    └─BatchNorm2d: 3-47            [-1, 512, 4, 4]           1,024\n|    |    └─ReLU: 3-48                   [-1, 512, 4, 4]           --\n|    └─MaxPool2d: 2-12                   [-1, 512, 2, 2]           --\n├─AdaptiveAvgPool2d: 1-5                 [-1, 512, 1, 1]           --\n├─Linear: 1-6                            [-1, 10]                  5,130\n==========================================================================================\nTotal params: 10,966,986\nTrainable params: 10,966,986\nNon-trainable params: 0\nTotal mult-adds (M): 533.29\n==========================================================================================\nInput size (MB): 0.01\nForward/backward pass size (MB): 7.50\nParams size (MB): 41.84\nEstimated Total Size (MB): 49.35\n=========================================================================================="
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if device == 'cuda':\n",
        "    resnet = torch.compile(resnet)\n",
        "optim = Adam(resnet.parameters(), lr=1e-3)\n",
        "\n",
        "results = train_model(resnet, trainloader, testloader, optim, n_epochs=30)\n",
        "plot_history(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "6Nf5QUmowGZV",
        "outputId": "911e9fd8-0ef6-4695-802a-c7c31164f7f0",
        "execution": {
          "iopub.status.busy": "2024-09-16T22:49:17.373246Z",
          "iopub.execute_input": "2024-09-16T22:49:17.373635Z",
          "iopub.status.idle": "2024-09-16T23:07:06.920649Z",
          "shell.execute_reply.started": "2024-09-16T22:49:17.373597Z",
          "shell.execute_reply": "2024-09-16T23:07:06.919441Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "text": "Training Accuracy 97.21% | Validation Accuracy 90.17% : 100%|██████████| 30/30 [17:49<00:00, 35.64s/it]\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x500 with 2 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see both networks are identical except for the residual connections. Even for small networks such as these two you can see the improvement due to the residual connections. For larger networks with 50, 100, etc layers, this gap in performance will become wider."
      ],
      "metadata": {
        "id": "18ejGc9ZIBv_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Transfer Learning"
      ],
      "metadata": {
        "id": "ATVheApKgEbJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this section we are going to learn about transfer learning.\n",
        "\n",
        "We use models available from torchvision. You can find a list of all [available models and pre-trained weights](https://pytorch.org/vision/stable/models.html) on torchvision.\n",
        "\n",
        "To observe the improvement caused by transfer learning, we train the `ResNet18` model twice, once with pre-trained weights (from the ImageNet dataset) and once without pre-training."
      ],
      "metadata": {
        "id": "DyWC67CaIjHg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "resnet18 = models.resnet18(weights='IMAGENET1K_V1')\n",
        "num_ftrs = resnet18.fc.in_features\n",
        "resnet18.fc = nn.Linear(num_ftrs, 10)\n",
        "\n",
        "resnet18 = resnet18.to(device)\n",
        "summary(resnet18, (3, 32, 32))"
      ],
      "metadata": {
        "id": "ph6xn58bwOvs",
        "execution": {
          "iopub.status.busy": "2024-09-16T23:07:06.922319Z",
          "iopub.execute_input": "2024-09-16T23:07:06.922662Z",
          "iopub.status.idle": "2024-09-16T23:07:07.529220Z",
          "shell.execute_reply.started": "2024-09-16T23:07:06.922626Z",
          "shell.execute_reply": "2024-09-16T23:07:07.528232Z"
        },
        "trusted": true,
        "outputId": "2ff28cc7-795b-4849-9d2b-86c2a08e702d"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "text": "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n100%|██████████| 44.7M/44.7M [00:00<00:00, 188MB/s]\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "==========================================================================================\nLayer (type:depth-idx)                   Output Shape              Param #\n==========================================================================================\n├─Conv2d: 1-1                            [-1, 64, 16, 16]          9,408\n├─BatchNorm2d: 1-2                       [-1, 64, 16, 16]          128\n├─ReLU: 1-3                              [-1, 64, 16, 16]          --\n├─MaxPool2d: 1-4                         [-1, 64, 8, 8]            --\n├─Sequential: 1-5                        [-1, 64, 8, 8]            --\n|    └─BasicBlock: 2-1                   [-1, 64, 8, 8]            --\n|    |    └─Conv2d: 3-1                  [-1, 64, 8, 8]            36,864\n|    |    └─BatchNorm2d: 3-2             [-1, 64, 8, 8]            128\n|    |    └─ReLU: 3-3                    [-1, 64, 8, 8]            --\n|    |    └─Conv2d: 3-4                  [-1, 64, 8, 8]            36,864\n|    |    └─BatchNorm2d: 3-5             [-1, 64, 8, 8]            128\n|    |    └─ReLU: 3-6                    [-1, 64, 8, 8]            --\n|    └─BasicBlock: 2-2                   [-1, 64, 8, 8]            --\n|    |    └─Conv2d: 3-7                  [-1, 64, 8, 8]            36,864\n|    |    └─BatchNorm2d: 3-8             [-1, 64, 8, 8]            128\n|    |    └─ReLU: 3-9                    [-1, 64, 8, 8]            --\n|    |    └─Conv2d: 3-10                 [-1, 64, 8, 8]            36,864\n|    |    └─BatchNorm2d: 3-11            [-1, 64, 8, 8]            128\n|    |    └─ReLU: 3-12                   [-1, 64, 8, 8]            --\n├─Sequential: 1-6                        [-1, 128, 4, 4]           --\n|    └─BasicBlock: 2-3                   [-1, 128, 4, 4]           --\n|    |    └─Conv2d: 3-13                 [-1, 128, 4, 4]           73,728\n|    |    └─BatchNorm2d: 3-14            [-1, 128, 4, 4]           256\n|    |    └─ReLU: 3-15                   [-1, 128, 4, 4]           --\n|    |    └─Conv2d: 3-16                 [-1, 128, 4, 4]           147,456\n|    |    └─BatchNorm2d: 3-17            [-1, 128, 4, 4]           256\n|    |    └─Sequential: 3-18             [-1, 128, 4, 4]           8,448\n|    |    └─ReLU: 3-19                   [-1, 128, 4, 4]           --\n|    └─BasicBlock: 2-4                   [-1, 128, 4, 4]           --\n|    |    └─Conv2d: 3-20                 [-1, 128, 4, 4]           147,456\n|    |    └─BatchNorm2d: 3-21            [-1, 128, 4, 4]           256\n|    |    └─ReLU: 3-22                   [-1, 128, 4, 4]           --\n|    |    └─Conv2d: 3-23                 [-1, 128, 4, 4]           147,456\n|    |    └─BatchNorm2d: 3-24            [-1, 128, 4, 4]           256\n|    |    └─ReLU: 3-25                   [-1, 128, 4, 4]           --\n├─Sequential: 1-7                        [-1, 256, 2, 2]           --\n|    └─BasicBlock: 2-5                   [-1, 256, 2, 2]           --\n|    |    └─Conv2d: 3-26                 [-1, 256, 2, 2]           294,912\n|    |    └─BatchNorm2d: 3-27            [-1, 256, 2, 2]           512\n|    |    └─ReLU: 3-28                   [-1, 256, 2, 2]           --\n|    |    └─Conv2d: 3-29                 [-1, 256, 2, 2]           589,824\n|    |    └─BatchNorm2d: 3-30            [-1, 256, 2, 2]           512\n|    |    └─Sequential: 3-31             [-1, 256, 2, 2]           33,280\n|    |    └─ReLU: 3-32                   [-1, 256, 2, 2]           --\n|    └─BasicBlock: 2-6                   [-1, 256, 2, 2]           --\n|    |    └─Conv2d: 3-33                 [-1, 256, 2, 2]           589,824\n|    |    └─BatchNorm2d: 3-34            [-1, 256, 2, 2]           512\n|    |    └─ReLU: 3-35                   [-1, 256, 2, 2]           --\n|    |    └─Conv2d: 3-36                 [-1, 256, 2, 2]           589,824\n|    |    └─BatchNorm2d: 3-37            [-1, 256, 2, 2]           512\n|    |    └─ReLU: 3-38                   [-1, 256, 2, 2]           --\n├─Sequential: 1-8                        [-1, 512, 1, 1]           --\n|    └─BasicBlock: 2-7                   [-1, 512, 1, 1]           --\n|    |    └─Conv2d: 3-39                 [-1, 512, 1, 1]           1,179,648\n|    |    └─BatchNorm2d: 3-40            [-1, 512, 1, 1]           1,024\n|    |    └─ReLU: 3-41                   [-1, 512, 1, 1]           --\n|    |    └─Conv2d: 3-42                 [-1, 512, 1, 1]           2,359,296\n|    |    └─BatchNorm2d: 3-43            [-1, 512, 1, 1]           1,024\n|    |    └─Sequential: 3-44             [-1, 512, 1, 1]           132,096\n|    |    └─ReLU: 3-45                   [-1, 512, 1, 1]           --\n|    └─BasicBlock: 2-8                   [-1, 512, 1, 1]           --\n|    |    └─Conv2d: 3-46                 [-1, 512, 1, 1]           2,359,296\n|    |    └─BatchNorm2d: 3-47            [-1, 512, 1, 1]           1,024\n|    |    └─ReLU: 3-48                   [-1, 512, 1, 1]           --\n|    |    └─Conv2d: 3-49                 [-1, 512, 1, 1]           2,359,296\n|    |    └─BatchNorm2d: 3-50            [-1, 512, 1, 1]           1,024\n|    |    └─ReLU: 3-51                   [-1, 512, 1, 1]           --\n├─AdaptiveAvgPool2d: 1-9                 [-1, 512, 1, 1]           --\n├─Linear: 1-10                           [-1, 10]                  5,130\n==========================================================================================\nTotal params: 11,181,642\nTrainable params: 11,181,642\nNon-trainable params: 0\nTotal mult-adds (M): 59.52\n==========================================================================================\nInput size (MB): 0.01\nForward/backward pass size (MB): 0.77\nParams size (MB): 42.65\nEstimated Total Size (MB): 43.44\n==========================================================================================\n",
          "output_type": "stream"
        },
        {
          "execution_count": 11,
          "output_type": "execute_result",
          "data": {
            "text/plain": "==========================================================================================\nLayer (type:depth-idx)                   Output Shape              Param #\n==========================================================================================\n├─Conv2d: 1-1                            [-1, 64, 16, 16]          9,408\n├─BatchNorm2d: 1-2                       [-1, 64, 16, 16]          128\n├─ReLU: 1-3                              [-1, 64, 16, 16]          --\n├─MaxPool2d: 1-4                         [-1, 64, 8, 8]            --\n├─Sequential: 1-5                        [-1, 64, 8, 8]            --\n|    └─BasicBlock: 2-1                   [-1, 64, 8, 8]            --\n|    |    └─Conv2d: 3-1                  [-1, 64, 8, 8]            36,864\n|    |    └─BatchNorm2d: 3-2             [-1, 64, 8, 8]            128\n|    |    └─ReLU: 3-3                    [-1, 64, 8, 8]            --\n|    |    └─Conv2d: 3-4                  [-1, 64, 8, 8]            36,864\n|    |    └─BatchNorm2d: 3-5             [-1, 64, 8, 8]            128\n|    |    └─ReLU: 3-6                    [-1, 64, 8, 8]            --\n|    └─BasicBlock: 2-2                   [-1, 64, 8, 8]            --\n|    |    └─Conv2d: 3-7                  [-1, 64, 8, 8]            36,864\n|    |    └─BatchNorm2d: 3-8             [-1, 64, 8, 8]            128\n|    |    └─ReLU: 3-9                    [-1, 64, 8, 8]            --\n|    |    └─Conv2d: 3-10                 [-1, 64, 8, 8]            36,864\n|    |    └─BatchNorm2d: 3-11            [-1, 64, 8, 8]            128\n|    |    └─ReLU: 3-12                   [-1, 64, 8, 8]            --\n├─Sequential: 1-6                        [-1, 128, 4, 4]           --\n|    └─BasicBlock: 2-3                   [-1, 128, 4, 4]           --\n|    |    └─Conv2d: 3-13                 [-1, 128, 4, 4]           73,728\n|    |    └─BatchNorm2d: 3-14            [-1, 128, 4, 4]           256\n|    |    └─ReLU: 3-15                   [-1, 128, 4, 4]           --\n|    |    └─Conv2d: 3-16                 [-1, 128, 4, 4]           147,456\n|    |    └─BatchNorm2d: 3-17            [-1, 128, 4, 4]           256\n|    |    └─Sequential: 3-18             [-1, 128, 4, 4]           8,448\n|    |    └─ReLU: 3-19                   [-1, 128, 4, 4]           --\n|    └─BasicBlock: 2-4                   [-1, 128, 4, 4]           --\n|    |    └─Conv2d: 3-20                 [-1, 128, 4, 4]           147,456\n|    |    └─BatchNorm2d: 3-21            [-1, 128, 4, 4]           256\n|    |    └─ReLU: 3-22                   [-1, 128, 4, 4]           --\n|    |    └─Conv2d: 3-23                 [-1, 128, 4, 4]           147,456\n|    |    └─BatchNorm2d: 3-24            [-1, 128, 4, 4]           256\n|    |    └─ReLU: 3-25                   [-1, 128, 4, 4]           --\n├─Sequential: 1-7                        [-1, 256, 2, 2]           --\n|    └─BasicBlock: 2-5                   [-1, 256, 2, 2]           --\n|    |    └─Conv2d: 3-26                 [-1, 256, 2, 2]           294,912\n|    |    └─BatchNorm2d: 3-27            [-1, 256, 2, 2]           512\n|    |    └─ReLU: 3-28                   [-1, 256, 2, 2]           --\n|    |    └─Conv2d: 3-29                 [-1, 256, 2, 2]           589,824\n|    |    └─BatchNorm2d: 3-30            [-1, 256, 2, 2]           512\n|    |    └─Sequential: 3-31             [-1, 256, 2, 2]           33,280\n|    |    └─ReLU: 3-32                   [-1, 256, 2, 2]           --\n|    └─BasicBlock: 2-6                   [-1, 256, 2, 2]           --\n|    |    └─Conv2d: 3-33                 [-1, 256, 2, 2]           589,824\n|    |    └─BatchNorm2d: 3-34            [-1, 256, 2, 2]           512\n|    |    └─ReLU: 3-35                   [-1, 256, 2, 2]           --\n|    |    └─Conv2d: 3-36                 [-1, 256, 2, 2]           589,824\n|    |    └─BatchNorm2d: 3-37            [-1, 256, 2, 2]           512\n|    |    └─ReLU: 3-38                   [-1, 256, 2, 2]           --\n├─Sequential: 1-8                        [-1, 512, 1, 1]           --\n|    └─BasicBlock: 2-7                   [-1, 512, 1, 1]           --\n|    |    └─Conv2d: 3-39                 [-1, 512, 1, 1]           1,179,648\n|    |    └─BatchNorm2d: 3-40            [-1, 512, 1, 1]           1,024\n|    |    └─ReLU: 3-41                   [-1, 512, 1, 1]           --\n|    |    └─Conv2d: 3-42                 [-1, 512, 1, 1]           2,359,296\n|    |    └─BatchNorm2d: 3-43            [-1, 512, 1, 1]           1,024\n|    |    └─Sequential: 3-44             [-1, 512, 1, 1]           132,096\n|    |    └─ReLU: 3-45                   [-1, 512, 1, 1]           --\n|    └─BasicBlock: 2-8                   [-1, 512, 1, 1]           --\n|    |    └─Conv2d: 3-46                 [-1, 512, 1, 1]           2,359,296\n|    |    └─BatchNorm2d: 3-47            [-1, 512, 1, 1]           1,024\n|    |    └─ReLU: 3-48                   [-1, 512, 1, 1]           --\n|    |    └─Conv2d: 3-49                 [-1, 512, 1, 1]           2,359,296\n|    |    └─BatchNorm2d: 3-50            [-1, 512, 1, 1]           1,024\n|    |    └─ReLU: 3-51                   [-1, 512, 1, 1]           --\n├─AdaptiveAvgPool2d: 1-9                 [-1, 512, 1, 1]           --\n├─Linear: 1-10                           [-1, 10]                  5,130\n==========================================================================================\nTotal params: 11,181,642\nTrainable params: 11,181,642\nNon-trainable params: 0\nTotal mult-adds (M): 59.52\n==========================================================================================\nInput size (MB): 0.01\nForward/backward pass size (MB): 0.77\nParams size (MB): 42.65\nEstimated Total Size (MB): 43.44\n=========================================================================================="
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pre-Trained ResNet18"
      ],
      "metadata": {
        "id": "3ElgJ76UJ5jq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if device == 'cuda':\n",
        "    resnet18 = torch.compile(resnet18)\n",
        "optim = Adam(resnet18.parameters(), lr=1e-3)\n",
        "\n",
        "results = train_model(resnet18, trainloader, testloader, optim, n_epochs=10)\n",
        "plot_history(results)"
      ],
      "metadata": {
        "id": "norUrdhkyzyO",
        "execution": {
          "iopub.status.busy": "2024-09-16T23:07:07.530519Z",
          "iopub.execute_input": "2024-09-16T23:07:07.530923Z",
          "iopub.status.idle": "2024-09-16T23:09:54.190299Z",
          "shell.execute_reply.started": "2024-09-16T23:07:07.530878Z",
          "shell.execute_reply": "2024-09-16T23:09:54.189206Z"
        },
        "trusted": true,
        "outputId": "df686236-68fc-47f0-cb1e-63e10b377b75"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "text": "Training Accuracy 85.13% | Validation Accuracy 83.93% : 100%|██████████| 10/10 [02:46<00:00, 16.61s/it]\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x500 with 2 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Normal ResNet18"
      ],
      "metadata": {
        "id": "8vwoikzNKCe_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "resnet18 = models.resnet18(weights=None)\n",
        "num_ftrs = resnet18.fc.in_features\n",
        "resnet18.fc = nn.Linear(num_ftrs, 10)\n",
        "\n",
        "resnet18 = resnet18.to(device)\n",
        "if device == 'cuda':\n",
        "    resnet18 = torch.compile(resnet18)\n",
        "optim = Adam(resnet18.parameters(), lr=1e-3)\n",
        "\n",
        "results = train_model(resnet18, trainloader, testloader, optim, n_epochs=10)\n",
        "plot_history(results)"
      ],
      "metadata": {
        "id": "eyLwP0maywKC",
        "execution": {
          "iopub.status.busy": "2024-09-16T23:09:54.191754Z",
          "iopub.execute_input": "2024-09-16T23:09:54.192141Z",
          "iopub.status.idle": "2024-09-16T23:12:37.988038Z",
          "shell.execute_reply.started": "2024-09-16T23:09:54.192096Z",
          "shell.execute_reply": "2024-09-16T23:12:37.986994Z"
        },
        "trusted": true,
        "outputId": "afd124f4-cfde-4353-f8ca-c78fa0c1638e"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "text": "Training Accuracy 77.88% | Validation Accuracy 76.85% : 100%|██████████| 10/10 [02:43<00:00, 16.32s/it]\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x500 with 2 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Results"
      ],
      "metadata": {
        "id": "c7KG-XRDKGaJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see the pre-trained weights improve the accuracy significantly."
      ],
      "metadata": {
        "id": "qUMJfOsKKHzT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Refrences\n",
        "\n",
        "*   [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)\n",
        "*   [Transfer Learning for Computer Vision Tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html)\n",
        "\n"
      ],
      "metadata": {
        "id": "1YQcszUnTpFf"
      }
    }
  ]
}
